Estimation and simulation of fractional Ornstein-Uhlenbeck processes, using deep neural networks
There is now a great deal of interest in parameter estimation of Ornstein-Uhlenbeck processes in finance because of its modelling capability (see e.g. [1]). That is why we investigated prediction of the unknown parameters with neural networks in the hope that we will obtain more accurate estimators than the ones […]
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